BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking.
BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking.
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DOI：
10.7554/elife.74314
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发表时间：
2022-08-23
期刊：
影响因子：
7.7
通讯作者：
DeNardo, Laura A.
中科院分区：
文献类型：
--
作者：
Gabriel, Christopher J.;Zeidler, Zachary;Jin, Benita;Guo, Changliang;Goodpaster, Caitlin M.;Kashay, Adrienne Q.;Wu, Anna;Delaney, Molly;Cheung, Jovian;DiFazio, Lauren E.;Sharpe, Melissa J.;Aharoni, Daniel;Wilke, Scott A.;DeNardo, Laura A.
关键词：
动物行为的定量描述对于研究认知和情感过程的神经基础是必不可少的。对自然行为的分析通常是手工完成的，或者使用昂贵的、不灵活的商业软件。最近，用于无标记姿态估计的机器学习方法实现了对自由移动动物的自动跟踪，包括在编码专业知识有限的实验室中。然而，基于姿势数据对特定行为进行分类需要额外的计算分析，并且对于许多群体来说仍然是一个重大挑战。我们开发了BehaviorDEPOT（基于位置跟踪的解码行为），这是一个简单，灵活的软件程序，可以从视频时间序列中检测行为，并可以分析实验测定的结果。BehaviorDEPOT从关键点跟踪数据中计算运动学和姿势统计数据，并创建可靠检测行为的方法。它不需要编程经验，适用于广泛的行为和实验设计。我们提供了几个硬编码的语法。我们的冻结检测启发式算法在小鼠和大鼠的视频中达到了90%以上的准确率，包括那些戴着栓系头戴的视频。BehaviorDEPOT还帮助研究人员开发他们自己的算法，并将其纳入软件的图形界面。行为数据按帧存储，以便与神经数据轻松对齐。我们展示了BehaviorDEPOT的即时效用和灵活性，使用流行的检测方法，包括恐惧条件反射，在T型迷宫中的决策，开放的领域，高架加迷宫，和新的对象探索。
Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software’s graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.